Dean Buonomano: State-dependent Networks: Timing and Computations Based on Neural Dynamics and Short-term Plasticity
Talk by Dean Buonomano from UCLA. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley.
The brain’s ability to seamlessly assimilate and process spatial and temporal information is critical to most behaviors, from understanding speech to playing the piano. Indeed, because the brain evolved to navigate a dynamic world, timing and temporal processing represent a fundamental computation. We have proposed that timing and the processing of temporal information emerges from the interaction between incoming stimuli and the internal state of neural networks. The internal state, is defined not only by ongoing activity (the active state) but by time-varying synaptic properties, such as short-term synaptic plasticity (the hidden state). One prediction of this hypothesis is that timing is a general property of cortical circuits. We provide evidence in this direction by demonstrating that in vitro cortical networks can “learn” simple temporal patterns. Finally, previous theoretical studies have suggested that recurrent networks capable of self-perpetuating activity hold significant computational potential. However, harnessing the computational potential of these networks has been hampered by the fact that such networks are chaotic. We show that it is possible to “tame” chaos through recurrent plasticity, and create a novel and powerful general framework for how cortical circuits compute.